PMC:7417788 / 64374-66944 JSON TXT

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LitCovid-PMC-OGER-BB

{"project":"LitCovid-PMC-OGER-BB","denotations":[{"id":"T1425","span":{"begin":64,"end":72},"obj":"CHEBI:28073;CHEBI:28073"},{"id":"T1426","span":{"begin":114,"end":122},"obj":"CHEBI:28073;CHEBI:28073"},{"id":"T1427","span":{"begin":346,"end":354},"obj":"CHEBI:28073;CHEBI:28073"},{"id":"T1428","span":{"begin":373,"end":380},"obj":"CHEBI:33893;CHEBI:33893"},{"id":"T1429","span":{"begin":919,"end":923},"obj":"SO:0000704;GO:0010467"},{"id":"T1430","span":{"begin":924,"end":934},"obj":"GO:0010467"},{"id":"T1431","span":{"begin":996,"end":1006},"obj":"GO:0010467"},{"id":"T1432","span":{"begin":1351,"end":1358},"obj":"GO:0010467"},{"id":"T1433","span":{"begin":1359,"end":1362},"obj":"PR:000008457"},{"id":"T1434","span":{"begin":1364,"end":1368},"obj":"PR:000008856"},{"id":"T1435","span":{"begin":1404,"end":1415},"obj":"SO:0000673"},{"id":"T1436","span":{"begin":1441,"end":1444},"obj":"CL:0000232"},{"id":"T1437","span":{"begin":1502,"end":1512},"obj":"GO:0010467"},{"id":"T1438","span":{"begin":1513,"end":1518},"obj":"SO:0000704"},{"id":"T1439","span":{"begin":1566,"end":1574},"obj":"SO:0001026"},{"id":"T1440","span":{"begin":2022,"end":2027},"obj":"SO:0000704"},{"id":"T1441","span":{"begin":2096,"end":2108},"obj":"GO:0005739"},{"id":"T1442","span":{"begin":2119,"end":2128},"obj":"GO:0005840"},{"id":"T1443","span":{"begin":2143,"end":2148},"obj":"SO:0000704"},{"id":"T1444","span":{"begin":2501,"end":2506},"obj":"SO:0000704"}],"text":"scRNA-seq data alignment, processing and sample aggregation\nThe Chromium Single Cell 5′ Library (the 10x Genomics chromium platform Illumina NovaSeq6000), Gel Bead and Multiplex Kit, and Chip Kit (10x Genomics) were used to convert single-cell suspension samples to barcoded scRNA-seq libraries. Single-cell RNA libraries were prepared using the Chromium Single Cell 5′ v2 Reagent (10x Genomics, 120237) kit as per the manufacturer’s protocols. The quality of the libraries was checked using the FastQC software. Initial processing of the sequenced data was performed using CellRanger software (https://support.10xgenomics.com, version 3.1.0).\nThe command Cell Ranger count in CellRanger Software Suite (10x Genomics) was used to demultiplex and barcode the sequences derived from the 10x Genomics single-cell RNA-seq platform. The data was filtered, normalized, dimensionality was reduced, clustered, and differential gene expression analysis were performed after calculation of the single-cell expression matrix by CellRanger using Python (version 3.7.7) Scanpy (https://scanpy.readthedocs.io/en/stable/index.html, version 1.4.6). Data collection and the subsequent analyses were performed in an unsupervised manner, but not blinded to the conditions of the experiments. For quality control, the filtered cell population was mainly those cells that express HBB, HBA1, and several light and heavy chain transcripts, which identified as the RBC-contaminated cell population. Likewise, several clusters expressing genes has no significance (P ≥ 0.1, calculate by 10x genomics Loupe Cell Browser with it default algorithm. P values are adjusted using the Benjamini-Hochberg correction for multiple tests) were removed. A total of 16 libraries were sequenced, and 166,609 cells (YA 77,652 cells, AA 88,957 cells) were analyzed after quality control in cohort-1. For cohort-3, 22 libraries and 205,434 cells (YH 79,039 cells, AH 88,750 cells, YCR 19,533 cells, ACR 18,112 cells) were remained for the subsequent analysis. The genes used in principal component analysis (PCA) analysis have eliminated mitochondria (MT), and ribosomes (RPL and RPS) genes with 50 principal components, and then aligned together, followed by t-distributed stochastic neighbor embedding (t-SNE) are both used after the results of the aligned. And using the run_harmony function (in pyharmony package, version 1.0.7) and combat function (in Scanpy) methods to deal with batch effect issues if batch effect existing in dataset. Genes not detected in any cell were removed from subsequent analysis."}

LitCovid-PD-FMA-UBERON

LitCovid-PD-MONDO

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T138","span":{"begin":2265,"end":2268},"obj":"Disease"}],"attributes":[{"id":"A138","pred":"mondo_id","subj":"T138","obj":"http://purl.obolibrary.org/obo/MONDO_0018859"}],"text":"scRNA-seq data alignment, processing and sample aggregation\nThe Chromium Single Cell 5′ Library (the 10x Genomics chromium platform Illumina NovaSeq6000), Gel Bead and Multiplex Kit, and Chip Kit (10x Genomics) were used to convert single-cell suspension samples to barcoded scRNA-seq libraries. Single-cell RNA libraries were prepared using the Chromium Single Cell 5′ v2 Reagent (10x Genomics, 120237) kit as per the manufacturer’s protocols. The quality of the libraries was checked using the FastQC software. Initial processing of the sequenced data was performed using CellRanger software (https://support.10xgenomics.com, version 3.1.0).\nThe command Cell Ranger count in CellRanger Software Suite (10x Genomics) was used to demultiplex and barcode the sequences derived from the 10x Genomics single-cell RNA-seq platform. The data was filtered, normalized, dimensionality was reduced, clustered, and differential gene expression analysis were performed after calculation of the single-cell expression matrix by CellRanger using Python (version 3.7.7) Scanpy (https://scanpy.readthedocs.io/en/stable/index.html, version 1.4.6). Data collection and the subsequent analyses were performed in an unsupervised manner, but not blinded to the conditions of the experiments. For quality control, the filtered cell population was mainly those cells that express HBB, HBA1, and several light and heavy chain transcripts, which identified as the RBC-contaminated cell population. Likewise, several clusters expressing genes has no significance (P ≥ 0.1, calculate by 10x genomics Loupe Cell Browser with it default algorithm. P values are adjusted using the Benjamini-Hochberg correction for multiple tests) were removed. A total of 16 libraries were sequenced, and 166,609 cells (YA 77,652 cells, AA 88,957 cells) were analyzed after quality control in cohort-1. For cohort-3, 22 libraries and 205,434 cells (YH 79,039 cells, AH 88,750 cells, YCR 19,533 cells, ACR 18,112 cells) were remained for the subsequent analysis. The genes used in principal component analysis (PCA) analysis have eliminated mitochondria (MT), and ribosomes (RPL and RPS) genes with 50 principal components, and then aligned together, followed by t-distributed stochastic neighbor embedding (t-SNE) are both used after the results of the aligned. And using the run_harmony function (in pyharmony package, version 1.0.7) and combat function (in Scanpy) methods to deal with batch effect issues if batch effect existing in dataset. Genes not detected in any cell were removed from subsequent analysis."}

LitCovid-PD-CLO

LitCovid-PD-CHEBI

LitCovid-PD-GO-BP

{"project":"LitCovid-PD-GO-BP","denotations":[{"id":"T296","span":{"begin":919,"end":934},"obj":"http://purl.obolibrary.org/obo/GO_0010467"}],"text":"scRNA-seq data alignment, processing and sample aggregation\nThe Chromium Single Cell 5′ Library (the 10x Genomics chromium platform Illumina NovaSeq6000), Gel Bead and Multiplex Kit, and Chip Kit (10x Genomics) were used to convert single-cell suspension samples to barcoded scRNA-seq libraries. Single-cell RNA libraries were prepared using the Chromium Single Cell 5′ v2 Reagent (10x Genomics, 120237) kit as per the manufacturer’s protocols. The quality of the libraries was checked using the FastQC software. Initial processing of the sequenced data was performed using CellRanger software (https://support.10xgenomics.com, version 3.1.0).\nThe command Cell Ranger count in CellRanger Software Suite (10x Genomics) was used to demultiplex and barcode the sequences derived from the 10x Genomics single-cell RNA-seq platform. The data was filtered, normalized, dimensionality was reduced, clustered, and differential gene expression analysis were performed after calculation of the single-cell expression matrix by CellRanger using Python (version 3.7.7) Scanpy (https://scanpy.readthedocs.io/en/stable/index.html, version 1.4.6). Data collection and the subsequent analyses were performed in an unsupervised manner, but not blinded to the conditions of the experiments. For quality control, the filtered cell population was mainly those cells that express HBB, HBA1, and several light and heavy chain transcripts, which identified as the RBC-contaminated cell population. Likewise, several clusters expressing genes has no significance (P ≥ 0.1, calculate by 10x genomics Loupe Cell Browser with it default algorithm. P values are adjusted using the Benjamini-Hochberg correction for multiple tests) were removed. A total of 16 libraries were sequenced, and 166,609 cells (YA 77,652 cells, AA 88,957 cells) were analyzed after quality control in cohort-1. For cohort-3, 22 libraries and 205,434 cells (YH 79,039 cells, AH 88,750 cells, YCR 19,533 cells, ACR 18,112 cells) were remained for the subsequent analysis. The genes used in principal component analysis (PCA) analysis have eliminated mitochondria (MT), and ribosomes (RPL and RPS) genes with 50 principal components, and then aligned together, followed by t-distributed stochastic neighbor embedding (t-SNE) are both used after the results of the aligned. And using the run_harmony function (in pyharmony package, version 1.0.7) and combat function (in Scanpy) methods to deal with batch effect issues if batch effect existing in dataset. Genes not detected in any cell were removed from subsequent analysis."}

LitCovid-sentences

{"project":"LitCovid-sentences","denotations":[{"id":"T356","span":{"begin":0,"end":59},"obj":"Sentence"},{"id":"T357","span":{"begin":60,"end":295},"obj":"Sentence"},{"id":"T358","span":{"begin":296,"end":444},"obj":"Sentence"},{"id":"T359","span":{"begin":445,"end":512},"obj":"Sentence"},{"id":"T360","span":{"begin":513,"end":643},"obj":"Sentence"},{"id":"T361","span":{"begin":644,"end":827},"obj":"Sentence"},{"id":"T362","span":{"begin":828,"end":1132},"obj":"Sentence"},{"id":"T363","span":{"begin":1133,"end":1272},"obj":"Sentence"},{"id":"T364","span":{"begin":1273,"end":1474},"obj":"Sentence"},{"id":"T365","span":{"begin":1475,"end":1620},"obj":"Sentence"},{"id":"T366","span":{"begin":1621,"end":1716},"obj":"Sentence"},{"id":"T367","span":{"begin":1717,"end":1858},"obj":"Sentence"},{"id":"T368","span":{"begin":1859,"end":2017},"obj":"Sentence"},{"id":"T369","span":{"begin":2018,"end":2317},"obj":"Sentence"},{"id":"T370","span":{"begin":2318,"end":2500},"obj":"Sentence"},{"id":"T371","span":{"begin":2501,"end":2570},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"scRNA-seq data alignment, processing and sample aggregation\nThe Chromium Single Cell 5′ Library (the 10x Genomics chromium platform Illumina NovaSeq6000), Gel Bead and Multiplex Kit, and Chip Kit (10x Genomics) were used to convert single-cell suspension samples to barcoded scRNA-seq libraries. Single-cell RNA libraries were prepared using the Chromium Single Cell 5′ v2 Reagent (10x Genomics, 120237) kit as per the manufacturer’s protocols. The quality of the libraries was checked using the FastQC software. Initial processing of the sequenced data was performed using CellRanger software (https://support.10xgenomics.com, version 3.1.0).\nThe command Cell Ranger count in CellRanger Software Suite (10x Genomics) was used to demultiplex and barcode the sequences derived from the 10x Genomics single-cell RNA-seq platform. The data was filtered, normalized, dimensionality was reduced, clustered, and differential gene expression analysis were performed after calculation of the single-cell expression matrix by CellRanger using Python (version 3.7.7) Scanpy (https://scanpy.readthedocs.io/en/stable/index.html, version 1.4.6). Data collection and the subsequent analyses were performed in an unsupervised manner, but not blinded to the conditions of the experiments. For quality control, the filtered cell population was mainly those cells that express HBB, HBA1, and several light and heavy chain transcripts, which identified as the RBC-contaminated cell population. Likewise, several clusters expressing genes has no significance (P ≥ 0.1, calculate by 10x genomics Loupe Cell Browser with it default algorithm. P values are adjusted using the Benjamini-Hochberg correction for multiple tests) were removed. A total of 16 libraries were sequenced, and 166,609 cells (YA 77,652 cells, AA 88,957 cells) were analyzed after quality control in cohort-1. For cohort-3, 22 libraries and 205,434 cells (YH 79,039 cells, AH 88,750 cells, YCR 19,533 cells, ACR 18,112 cells) were remained for the subsequent analysis. The genes used in principal component analysis (PCA) analysis have eliminated mitochondria (MT), and ribosomes (RPL and RPS) genes with 50 principal components, and then aligned together, followed by t-distributed stochastic neighbor embedding (t-SNE) are both used after the results of the aligned. And using the run_harmony function (in pyharmony package, version 1.0.7) and combat function (in Scanpy) methods to deal with batch effect issues if batch effect existing in dataset. Genes not detected in any cell were removed from subsequent analysis."}

LitCovid-PubTator

{"project":"LitCovid-PubTator","denotations":[{"id":"1445","span":{"begin":178,"end":181},"obj":"Gene"},{"id":"1446","span":{"begin":404,"end":407},"obj":"Gene"},{"id":"1447","span":{"begin":192,"end":195},"obj":"Gene"},{"id":"1448","span":{"begin":64,"end":72},"obj":"Chemical"},{"id":"1449","span":{"begin":114,"end":122},"obj":"Chemical"},{"id":"1453","span":{"begin":1359,"end":1362},"obj":"Gene"},{"id":"1454","span":{"begin":1364,"end":1368},"obj":"Gene"},{"id":"1455","span":{"begin":1922,"end":1924},"obj":"Disease"}],"attributes":[{"id":"A1445","pred":"tao:has_database_id","subj":"1445","obj":"Gene:3815"},{"id":"A1446","pred":"tao:has_database_id","subj":"1446","obj":"Gene:3815"},{"id":"A1447","pred":"tao:has_database_id","subj":"1447","obj":"Gene:3815"},{"id":"A1448","pred":"tao:has_database_id","subj":"1448","obj":"MESH:D002857"},{"id":"A1449","pred":"tao:has_database_id","subj":"1449","obj":"MESH:D002857"},{"id":"A1453","pred":"tao:has_database_id","subj":"1453","obj":"Gene:3043"},{"id":"A1454","pred":"tao:has_database_id","subj":"1454","obj":"Gene:3039"},{"id":"A1455","pred":"tao:has_database_id","subj":"1455","obj":"MESH:D007039"}],"namespaces":[{"prefix":"Tax","uri":"https://www.ncbi.nlm.nih.gov/taxonomy/"},{"prefix":"MESH","uri":"https://id.nlm.nih.gov/mesh/"},{"prefix":"Gene","uri":"https://www.ncbi.nlm.nih.gov/gene/"},{"prefix":"CVCL","uri":"https://web.expasy.org/cellosaurus/CVCL_"}],"text":"scRNA-seq data alignment, processing and sample aggregation\nThe Chromium Single Cell 5′ Library (the 10x Genomics chromium platform Illumina NovaSeq6000), Gel Bead and Multiplex Kit, and Chip Kit (10x Genomics) were used to convert single-cell suspension samples to barcoded scRNA-seq libraries. Single-cell RNA libraries were prepared using the Chromium Single Cell 5′ v2 Reagent (10x Genomics, 120237) kit as per the manufacturer’s protocols. The quality of the libraries was checked using the FastQC software. Initial processing of the sequenced data was performed using CellRanger software (https://support.10xgenomics.com, version 3.1.0).\nThe command Cell Ranger count in CellRanger Software Suite (10x Genomics) was used to demultiplex and barcode the sequences derived from the 10x Genomics single-cell RNA-seq platform. The data was filtered, normalized, dimensionality was reduced, clustered, and differential gene expression analysis were performed after calculation of the single-cell expression matrix by CellRanger using Python (version 3.7.7) Scanpy (https://scanpy.readthedocs.io/en/stable/index.html, version 1.4.6). Data collection and the subsequent analyses were performed in an unsupervised manner, but not blinded to the conditions of the experiments. For quality control, the filtered cell population was mainly those cells that express HBB, HBA1, and several light and heavy chain transcripts, which identified as the RBC-contaminated cell population. Likewise, several clusters expressing genes has no significance (P ≥ 0.1, calculate by 10x genomics Loupe Cell Browser with it default algorithm. P values are adjusted using the Benjamini-Hochberg correction for multiple tests) were removed. A total of 16 libraries were sequenced, and 166,609 cells (YA 77,652 cells, AA 88,957 cells) were analyzed after quality control in cohort-1. For cohort-3, 22 libraries and 205,434 cells (YH 79,039 cells, AH 88,750 cells, YCR 19,533 cells, ACR 18,112 cells) were remained for the subsequent analysis. The genes used in principal component analysis (PCA) analysis have eliminated mitochondria (MT), and ribosomes (RPL and RPS) genes with 50 principal components, and then aligned together, followed by t-distributed stochastic neighbor embedding (t-SNE) are both used after the results of the aligned. And using the run_harmony function (in pyharmony package, version 1.0.7) and combat function (in Scanpy) methods to deal with batch effect issues if batch effect existing in dataset. Genes not detected in any cell were removed from subsequent analysis."}

PMC:7417788 / 64374-66944 JSONTXT

Annnotations TAB JSON ListView MergeView

PMC:7417788 / 64374-66944 JSON TXT